Tan et al. in RTD-Net [1]} proposed a relaxed transformer to directly generate action proposals without the need to human prior knowledge for careful design of anchor placement or boundary matching mechanisms. The transformer encoder models long-range temporal context and captures inter-proposal relationships from a global view to precisely localize action instances. They also argued that the snippet features in a video change at a very slow speed and direct employment of self-attention in transformers can lead to over-smoothing. Therefore, they customized the encoder with a boundary-attentive architecture to enhance the discrimination capability of action boundary. Chang et al. in ATAG [2]} designed an augmented transformer to mine long-range temporal context for noisy action instance localization. The snippet-level features generated by transformer are used to classify the snippets to action or background under supervision of a binary classification loss. Throughout this process the transformer learns to capture long-term dependencies at snippet level.
